We present Sonoflex, a thin-form, embroidered dynamic speaker made without using a permanent magnet. Our design consists of two flat spiral coils, stacked on top of each other, and is based on an isolated, thin (0.15 mm) enameled copper wire. Our approach allows for thin, lightweight, and textile speakers and does not require high voltage as in electrostatic speakers. We show how the speaker can be designed and fabricated and evaluate its acoustic properties as a function of manufacturing parameters (size, turn counts, turn spacing, and substrate materials). The experiment results revealed that we can produce audible sound with a broad frequency range (1.5 kHz - 20 kHz) with the embroidered speaker with a diameter of 50 mm. We conclude the paper by presenting several applications such as audible notifications and near-ultrasound communication.

Integrating sensors into knitted input devices traditionally comes with considerable constraints for textile and UI design freedom. In this work, we demonstrate a novel, minimally invasive method for fabricating knitted sensors that overcomes this limitation. We integrate copper wire with piezoresistive enamel directly into the fabric using weft knitting to establish strain and pressure sensing cells that consist only of single pairs of intermeshed loops. The result is unobtrusive and potentially invisible, which provides tremendous latitude for visual and haptic design. Furthermore, we present several variations of stitch compositions, resulting in loop meshes that feature distinct response with respect to direction of exerting force. Utilizing this property, we are able to infer actuation modalities and considerably expand the device’s input space. In particular, we discern strain directions and surface pressure. Moreover, we provide an in-depth description of our fabrication method, and demonstrate our solution’s versatility on three exemplary use cases.

We present multi-component knitted resistive sensors for tracking tensile force. The knits were fabricated using a Twill structure, which is a simple pattern featuring anisotropic elastic behavior, providing high stability along course-direction. Our sensors are made of two commercially available conductive yarn types, with highly different linear resistance. We present a variety of integration methods using the proposed Twill structure, all of which can be easily replicated on a two-bed weft-knitting machine. We evaluate the performance of the resulting sensor variations, with respect to consistency, hysteresis, short-term and long-term relaxation and drift, among other metrics. We found that particulars of the knit’s loop composition have a crucial effect on the consistency of the sensor readings. Furthermore, we show that knitting resistive yarn more tightly than the substrate material gives superior results and that improving elastic recoil by adding Lycra to the supporting substrate can considerably improve performance.

Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.

We present a fully textile capacitive touch sensor that provides an additional electrode for implementing driven (or active) shielding, which can considerably improve signal-to-noise ratio (SNR) and guard from parasitic capacitance. While driven shields are state-of-the-art for traditional printed sensors, they are still uncommon in contemporary textile user interfaces. Using an enameled copper wire as a bobbin thread in computerized machine embroidery, both sensor and shield electrodes are applied in a single sequence, eliminating manual intermediate or finishing steps and harnessing the design flexibility provided by the embroidery technique. Preliminary finite element analysis indicates significant improvements gained by employing driven shields, in terms of sensing range as well as signal quality, when the sensor is close to other conductors, e.g., when worn on the user’s body. By varying the shield electrode’s pattern properties, such as pattern type, area, and density, we investigate their effects on the resulting SNR, based on characterizations within controlled lab experiments. A major finding of our work is that the impact of density seems minor, while adjustments of the pattern layout seem to adequately compensate for a lower stitch density, with a grid layout yielding the best results.

We present multi-component knitted resistive sensors for tracking tensile force. The knits were fabricated using a Twill structure, which is a simple pattern featuring anisotropic elastic behavior, providing high stability along course-direction. Our sensors are made of two commercially available conductive yarn types, with highly different linear resistance. We present a variety of integration methods using the proposed Twill structure, all of which can be easily replicated on a two-bed weft-knitting machine. We evaluate the performance of the resulting sensor variations, with respect to consistency, hysteresis, short-term and long-term relaxation and drift, among other metrics. We found that particulars of the knit's loop composition have a crucial effect on the consistency of the sensor readings. Furthermore, we show that knitting resistive yarn more tightly than the substrate material gives superior results and that improving elastic recoil by adding Lycra to the supporting substrate can considerably improve performance.